This is an open deep learning course made by Deep Learning School, Tinkoff and Catalyst team. Lectures and practice notebooks located in '''./week*''' folders. Homeworks are in '''./homework*''' folders.
- week 1: Into to deep learning
- Deep learning – introduction, backpropagation algorithm. Optimization methods
- Neural Network in numpy
- week 2: Deep learning frameworks
- Regularization methods and deep learning frameworks
- Pytorch basics & extras
- week 3: Convolutional Neural Network
- CNN. Model Zoo
- Convolutional kernels. ResNet. Simple Noise Attack
- week 4: Object Detection, Image Segmentation
- Object Detection. (One, Two)-Stage methods. Anchors.
- Image Segmentation. Up-scaling. FCN, U-net, FPN. DeepMask.
- week 5: Metric Learning
- Metric Learning. Contrastive and Triplet Loss. Samplers.
- Cross Entropy Loss modifications. SphereFace, CosFace, ArcFace.
- week 6: Autoencoders
- week 7: Generative Adversarial Models
- week 8: Natural Language Processing
- week 9: Attention and transformer model
- week 10: Advanced NLP
- week 11: Recommender System
- week 12: Reinforcement Learning for RecSys
- week 13: Engineering stuff for DL
- week 14: DL Best Practices